Controlling magnetic levitation systems is a significant challenge due to their inherent nonlinearity, open-loop instability, and high sensitivity to uncertainties. While classical controllers struggle to provide robust performance under these conditions, advanced intelligent controllers often lack a systematic method for parameter tuning, limiting their practical effectiveness. This paper addresses these challenges by applying and evaluating an adapted quantum-inspired genetic algorithm, in comparison to a standard genetic algorithm, for the optimization of interval type-2 fuzzy state feedback controllers for magnetic levitation servocontrol. The approach leverages the robustness of interval type-2 fuzzy controllers to handle imprecisions and the optimization capabilities of these quantum-inspired algorithms to systematically tune controller parameters. The system architecture integrates a nonlinear magnetic levitation model, local controllers designed via pole placement, and a fuzzy controller that combines these through parallel distributed compensation. Both algorithms optimize the controller’s desired poles to minimize the Integral of Time-weighted Absolute Error performance metric. Simulations compare the performance of type-1 and interval type-2 fuzzy controllers, optimized by both algorithms, against non-optimized versions. Results reveal a trade-off: the controller optimized by the standard genetic algorithm achieved the lowest Integral of Time-weighted Absolute Error (6.163e-04), an improvement of 61.05% over the original, while the controller optimized by the quantum-inspired genetic algorithm yielded the lowest Root Mean Square error (0.736 mm), though both exhibited a higher overshoot. The methodology offers a flexible, computationally efficient solution for complex nonlinear systems, with potential for industrial applications. Future work may focus on multi-objective optimization to reduce overshoot and on the direct tuning of the controller’s Footprint of Uncertainty, or integrate neural networks for improved dynamic performance.